Epidemiological study on the penicillin resistance of clinical Streptococcus pneumoniae isolates identified as the common sequence types.
Bibliographic record
Abstract
There were some limitation in the current interpretation about the penicillin resistance mechanism of clinical Streptococcus pneumoniae isolates at the strain level. To explore the possibilities of studying the mechanism based on the sequence types (ST) of this bacteria, 488 isolates collected in Beijing from 1997-2014 and 88 isolates collected in Youyang County, Chongqing and Zhongjiang County, Sichuan in 2015 were analyzed by penicillin minimum inhibitory concentration (MIC) distribution and annual distribution. The results showed that the penicillin MICs of the all isolates covering by the given ST in Beijing have a defined range, either <0.25 mg/L or≥0.25 mg/L, except for the ST342. The isolates with penicillin MIC <0.25 mg/L were mainly collected before 2001, after which the isolates with MIC≥0.25 mg/L occurred and became the major population gradually. This law of year distribution, however, was not obvious for any specific ST. The isolates covering by any given ST could be determined with different penicillin MICs in the first few years after it was identified. The penicillin MIC of isolates identified as common STs and collected in Youyang County, Chongqing and Sichuan Zhongjiang County, including the ST271, ST320 and ST81, was around 0.25~2 mg/L (≥0.25 mg/L). Our study revealed the epidemiological distribution of penicillin MICs of the given STs determined in clinical S. pneumoniae isolates, suggesting that it is reasonable to research the penicillin resistance mechanism based on the STs of this bacteria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".